where \(\theta ^{s}_j\)  and \(\theta ^{t}_j\)  represent the parameters of the \(j^{th}\)  layer of the source and target models, respectively. In [1]}, the idea is that domain-related knowledge is represented by the statistics of the bn layer [2]} (while the class-related knowledge is represented in the weight matrix). Basically, bn normalizes the mean and standard deviation for each individual feature channel such that each layer receives data from a similar distribution, regardless if it comes from the source or the target domain. Thus, bn is exploited to align the distribution, recomputing the mean and standard deviation in the target domain. Formally:
\(BN(X^t) = \lambda \left( \frac{x - \mu (X^t)}{\sigma (X^t)}\right) + \beta ,\) 
